Rising cybercriminal activities targeting enterprise intelligence assets and misusing individuals’ personally identifiable information (PII) highlight the need for improved corporate data governance standards. Accordingly, regulatory bodies worldwide have enforced laws and directives describing the requirements of adequate data protection methods. This post discusses building a robust data governance framework for organizational success.
What is a Data Governance Framework?
A data governance framework includes guidelines, workplace protocols, role descriptions, and technology recommendations to ensure your company’s business intelligence stays secure. It also educates stakeholders on legal aspects concerning data leaks, manipulated accounting data, and corporate intellectual property (IP) theft.
Multiple data governance solutions prioritizing cloud-based cybersecurity measures have emerged to fulfill the business world’s need for advanced data protection. Moreover, global data quality standardization bodies provide extensive literature relevant to enterprise data processes, like analytics, visualization, or benchmarking.
Examples of Data Governance Frameworks
Ex. 1| Data Governance Institute’s DGI Framework
DGI framework outlines why data governance is significant to successful business development. It describes what stakeholders can expect from enterprise data governance officers or DGOs. This approach categorizes the rules and assigns role-specific data protection accountability for coordinated compliance.
The farmwork defines focus areas by grouping performance metrics, data strategy objectives, and budgets. Furthermore, it recommends data steward roles to help DGOs streamline compliance-related activities. Other data stakeholders, like data consulting and analytics teams, will have unique decision rights for seamless control mechanisms.
Corporations utilizing the DGI framework for data governance align their policies and data stewardship efforts with legal requirements. They can identify, list, and resolve data quality issues while enhancing stakeholder communication. After all, respecting data subjects’ privacy rights is integral to an ideal governance framework. This framework also offers metrics to evaluate DGOs and data stewards’ roles.
Ex. 2| McKinsey’s Governance Framework
McKinsey’s data governance framework recommends creating a data council. It values rethinking each organizational process and addressing governance issues. Aside from domain-based role assignation, McKinsey recognizes that a data management officer (DMO) is vital for reliable governance policy implementation.
Importance of Optimizing Data Governance Framework
While Eckerson group has thirty-nine governance components, PwC has five. Meanwhile, Deloitte divides its data governance framework (DGF) into two sections. The former involves initial implementation components. On the other hand, the latter helps brands embrace the continuous governance improvement philosophy.
Likewise, each enterprise must study current frameworks to develop business-relevant DGF documentation. Remember, multiple companies in an industry might exhibit distinct vulnerabilities to governance and compliance risks. So, they require customized and optimized strategies to upgrade their data protection methods.
For instance, financial institutions are more likely to suffer from deliberate accounting and taxation data falsification. However, health insurance companies’ fraud incidents include manipulated patient history records. Both categories must tackle specific data integrity and validation problems. Neither can leverage identical data governance frameworks.
Building a Robust Data Governance Framework for Organizational Success
The following governance best practices can guide you in optimizing a DGF suitable for your enterprise processes.
1| Define Long-Term Goals and Short-Term Milestones
Discovering a governance strategy for particular business needs is time-consuming. On top of that, you will encounter several technological and psychological hurdles when encouraging stakeholders to adopt newer workflows.
As a result, focusing on long-term governance objectives might overwhelm your DGOs and data stewards. Instead, leaders can benefit from defining short-term milestones. Milestones help maintain team morale without deviating from long-term vision.
2| Identify Key Stakeholders
Training stakeholders to ensure immediate adoption is essential to robust governance implementation. After all, buying the best data protection tools will be a waste if your workers, customers, investors, and suppliers are uncomfortable using them.
You want to find professionals and customers who readily adapt to modern workflows. They can later transfer their skills to other stakeholders. Key stakeholders must understand how better governance creates business value to avoid miscommunication and attrition.
For example, enforcing multi-factor authentication might introduce additional delays in employee workflows. Similarly, encrypted networks and communication channels might be slower than unencrypted alternatives. However, encryption is crucial for business intelligence safety. So, you want tech-savvy stakeholders to inform others about the pros and cons of governance technologies to solve adoption fatigue issues.
3| Specify Roles and Accountabilities
Each organization must determine data governance and management roles to establish a transparent hierarchy of authority. Doing so prevents overlaps of responsibilities or order of duties. Consider the following aspects to nourish accountable data processing professionals.
Although DMO and DGO seem suitable roles for one person, separating them helps avoid potential conflicts of interest.
A data governance council must encourage multidisciplinary meetings for healthy exchanges of ideas among IT and non-IT professionals.
Another governance framework body must focus on creating, monitoring, and revising standard protocols. It must define data usage, analytics, storage, sharing, and deletion rules.
Data stewards must collaborate with all stakeholders to ensure employees follow appropriate practices.
Simultaneously, you will create data owner roles to regulate PII use cases, confidential reporting, and senior-level communications. A data owner can be an employee, an investor, a consumer, a supplier, a department, or an in-house researcher.
Data processors use, transform, sort, delete, and share data assets. Therefore, they must comply with governance frameworks to mitigate cybersecurity and integrity risks.
4| Evaluate Costs and Business-Relevance
Data operations can become expensive because your organization broadens the business analytics scope by entering new markets or acquiring other firms. The current infrastructure might reach its limits or bottlenecks as you gain more data. So, you will want to replace it with better equipment or migrate to the cloud.
If a few data operations lose business relevance, you can discontinue them. Consider whether omitting PII assets will have a negligible impact on your analytics. If you avoid privacy-invasive personal data gathering, you can reduce computer resource consumption dedicated to related analytical processes. You also increase your governance ratings by restricting PII usage.
Data governance officers have reasonable stakes in data quality management (DQM), where business relevance is critical. Therefore, DGOs and DMOs must work together to curb irrelevant data collection. Do not waste your company’s resources protecting non-essential data with no business advantage.
5| Develop and Monitor Performance Metrics
Demonstrating how a data governance framework reduces cybersecurity vulnerabilities and promotes better data quality will help secure future budgetary approvals. At the same time, you want to report inefficiencies, brainstorm solutions, and fix issues to upgrade your enterprise’s governance standards.
Performance metrics combine numerical progress quantifying measures with context-relevant conditions. They ultimately alert stakeholders on whether workers have met their targets and deadlines. Therefore, performance metrics help DGOs examine governance framework implementations.
For instance, encrypting business intelligence assets and data repositories in regional branch offices can be an objective. If some branches fail to encrypt their resources, they might need more funds and technical assistance. A metric describing the percentage progress of encryption initiatives at branch offices can enhance reporting.
Conclusion
Building a robust data governance framework involves listing stakeholders, specifying roles, training data stewards, optimizing costs, and documenting performance metrics. A DGO must oversee the on-ground implementation of the finalized framework and encourage stakeholders to keep a positive outlook on governance adoption.
Investing in improving your organization’s compliance ratings can result in legal and technological resilience. Since ethical investors prefer governance-complaint brands, you can get novel fundraising opportunities through a DGF implementation.
While the increasing sophistication of corporate espionage and ransomware attacks might threaten your business intelligence assets, governance helps mitigate those risks. Your team can get ideas from authoritative resources like the DGI framework or DAMA-DAMBoK2 for advanced compliance guidance. Otherwise, you can invite data governance experts to assist in developing business-relevant policies for organizational success.
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